The integration of continuous Zero Trust Architecture (ZTA) into Segment Routing over IPv6 (SRv6) networks introduces severe performance bottlenecks and physical constraints of the Maximum Transmission Unit (MTU). Specifically, naively embedding massive Zero-Knowledge Proof (ZKP) for per-packet authentication inevitably triggers catastrophic fragmentation and disrupts stateless forwarding. To address these fundamental limitations, this paper proposes a novel session-bound zero-knowledge authorization framework tailored for SRv6 programmable data planes. Our architecture explicitly decouples heavyweight cryptographic validations from the active forwarding path. Massive ZKP payloads are processed asynchronously via payload transmission in the control plane, while the data plane enforces line-rate access control using lightweight 32-byte capability tokens encapsulated in customized SRv6 extension headers. Furthermore, to mathematically balance robust security with forwarding efficiency, we formulate the dynamic verification process as a risk-aware Partially Observable Markov Decision Process (POMDP). Using in-band network telemetry, we derive an Adaptive Threshold Verification (ATV) algorithm that yields a closed-form <inline-formula> <tex-math notation="LaTeX">$O(1)$ </tex-math></inline-formula> complexity optimal scheduling policy. Extensive evaluations demonstrate that the decoupled mechanism seamlessly resolves the MTU bottleneck, maintaining stable baseline throughput under massive concurrent sessions. Concurrently, the ATV algorithm intelligently adapts to real-time threat intensities, conserving control-plane resources during safe periods while instantaneously triggering precise re-verifications against covert and volumetric cyberattacks.
Carlos Nú Nez-Gómez, VÃctor Garcia-Font, Helena Rifà -Pous, Muhammad Asad
The submitted work contains the following highlights: • We detail NxGenT, a decentralized reputation system for B5G and 6G networks. • We propose a three-phase reputation mechanism based on smart contracts. • We implement NxGenT and release its source code as open-source software. • We analyze NxGenT’s design and resilience against relevant trust attacks. • We evaluate NxGenT’s functionality and performance through experimental analysis. The evolution towards Beyond 5G (B5G) and 6G networks presents highly heterogeneous and dynamic scenarios in which numerous entities, including network operators, service providers and end users, interact in environments of mutual trust. However, the open nature of these networks poses significant challenges regarding security and trust, as traditional centralized mechanisms may prove inadequate or insufficient in such scenarios. In this context, decentralized trust systems are positioned as a promising solution to assess the reliability of entities participating in B5G and 6G networks, thereby enhancing decision-making processes and resilience of these environments. This paper introduces NxGenT , a decentralized reputation system based on blockchain and smart contracts for B5G/6G networks that guarantees the immutability and transparency of the collected evidence on entities’ performance and behavior, while decentralizing and automating the reputation mechanism. NxGenT is a decentralized, trustless system in which entities establish and verify compliance with Service Level Agreements (SLA) and provide feedback or subjective opinions about the entities they interact with in order to compute and assign reputation scores. To evaluate the proposal, we implement a local B5G testbed that deploys the primary components of this type of network, along with a second cloud-based testbed to analyze scalability in networks of different sizes. Finally, we contextualize NxGenT within the 6GENABLERS project as a representative use case of the proposed trust system, thus demonstrating its applicability in real scenarios.
With the rapid advancement of Web 3.0 technologies, public blockchain platforms are witnessing the emergence of novel services designed to enhance user privacy and anonymity. However, the powerful untraceability features inherent in these services inadvertently make them attractive tools for criminals seeking to launder illicit funds. Notably, existing de-anonymization methods face three major challenges when dealing with such transactions: highly homogenized transactional semantics, limited ability to model temporal discontinuities, and insufficient consideration of structural sparsity in account association graphs. To address these, we propose GradWATCH, designed to track anonymous accounts in Ethereum privacy-preserving services. Specifically, we first design a learnable account feature mapping module to extract informative transactional semantics from raw on-chain data. We then incorporate transaction relations into the account association graph to alleviate the adverse effects of structural sparsity. To capture temporal evolution, we further propose an edge-aware sliding-window mechanism that propagates and updates gradients at three granularities. Finally, we identify accounts controlled by the same entity by measuring their embedding distances in the learned representation space. Experimental results show that even under the conditions of unbalanced labels and sparse transactions, GradWATCH still achieves significant performance gains, with relative improvements ranging from 1.62% to 15. 22% in the MRR and from 3. 85% to 7. 31% in the F_1.
Digital identity verification often forces a privacy trade-off, where users must disclose sensitive personal data to prove simple eligibility criteria. As blockchain applications integrate with regulated environments, this over-disclosure creates significant risks of data breaches and surveillance. This work proposes a general Selective Disclosure Framework built on Ethereum, designed to decouple attribute verification from identity revelation. By utilizing client-side zk-SNARKs, the framework enables users to prove specific eligibility predicates without revealing underlying identity documents. We present a case study, ZK-Compliance, which implements a functional Grant, Verify, Revoke lifecycle for age verification. Preliminary results indicate that strict compliance requirements can be satisfied with negligible client-side latency (< 200 ms) while preserving the pseudonymous nature of public blockchains.
Automated negotiations in insurance and business-to-business (B2B) commerce encounter substantial challenges. Current systems force a trade-off between convenience and privacy by routing sensitive financial data through centralized servers, increasing security risks, and diminishing user trust. This study introduces a device-native autonomous Artificial Intelligence (AI) agent system for privacy-preserving negotiations. The proposed system operates exclusively on user hardware, enabling real-time bargaining while maintaining sensitive constraints locally. It integrates zero-knowledge proofs to ensure privacy and employs distilled world models to support advanced on-device reasoning. The architecture incorporates six technical components within an agentic AI workflow. Agents autonomously plan negotiation strategies, conduct secure multi-party bargaining, and generate cryptographic audit trails without exposing user data to external servers. The system is evaluated in insurance and B2B procurement scenarios across diverse device configurations. Results show an average success rate of 87%, a 2.4x latency improvement over cloud baselines, and strong privacy preservation through zero-knowledge proofs. User studies show 27% higher trust scores when decision trails are available. These findings establish a foundation for trustworthy autonomous agents in privacy-sensitive financial domains.
cloud computing environments and multi agent systems has presented huge difficulties in creating a trust system, verifying securely and largely being transparent amongst the heterogeneous entities. The traditional centralized methods continue to become unsuitable with their vulnerability to the single point of failure, breach of data and unimpeccable auditability. The decentralized and immutable nature of blockchain technology has become a promising solution, but the currently operational blockchain-based systems still present severe constraints which are associated with scalability, high computation cost, disturbing latency as well as absence of adaptive trust mechanism.The current paper suggests a set of new conceptual frameworks on the use of an efficient and secure blockchain-based trust and verification system adapted to the distributed environment. The model uses a hybrid design that combines on-chain and off-chain processing to make the performance efficient and do not compromise security. An active screening system of trust is presented to determine the trustworthiness of each of the participating nodes on the basis of transaction history, behavioral patterns as well as their success rate of validation. Also, it includes a featherweight hybrid consensus which is based on Proof of Stake (PoS) and Practical Byzantine Fault Tolerance (PBFT) to ensure that it uses less energy to execute and also enhance the speed of transactions verification.The framework also includes smart contracts to verify and control access and use of data array of cryptography methods to guarantee the integrity of data and authentication. The proposed model offers a practical and flexible solution to the current distributed system since it tackles major challenge related to the system, namely scalability, efficiency and security. The framework is applicable to various areas which have been showcased in the IoT networks, management of supply chains, data sharing in healthcare, and e-governance. Future research possibilities include incorporating the element of artificial intelligence in the adaptive trust and quantum-resistant cryptographic research.
The SPT-Txn Framework v6.0 integrates Attribute-Based Access Control (ABAC), Token-Based Access Control (TBAC), Non-Fungible Tokens (NFTs), Self-Sovereign Identity (SSI), Zero-Knowledge Decentralized Identifiers (zkDID), biometric uniqueness proofs, and IETF Transaction Tokens into a unified authorization architecture for the agentic economy. This working paper is developed in conjunction with IETF Internet-Draft draft-coetzee-oauth-spt-txn-tokens-00, available at https://datatracker.ietf.org/doc/draft-coetzee-oauth-spt-txn-tokens/
Decentralized and autonomous systems associated with Web 3.0 challenge long-standing assumptions about security governance, responsibility, and control. Although advances in cryptography, consensus mechanisms, and automation have strengthened technical protections, they have also fragmented accountability across software agents, organizations, and ecosystems, producing security failures in environments where responsibility for prevention, response, and remediation is unclear or contested. This paper demonstrates that many such failures stem not from inadequate technical safeguards but from governance gaps created by sociotechnical complexity. Drawing on sociotechnical systems theory, it introduces the LAG (Layers-Accountability-Governance) framework, which distinguishes among information technology security, information security, and enterprise-level governance, and clarifies the misalignment between ex ante preventive mechanisms and ex post response and recovery processes. Through analysis of decentralized architectures, autonomous agents, and machine identities, and case studies of the DAO, Poly Network, and oracle-related failures, the paper shows how technically correct system behavior can nonetheless produce governance failure and discusses implications for the design and governance of secure systems in complex digital ecosystems where traditional organizational boundaries no longer apply.
Recent advances in distributed system design have shifted from centralized client–server models to decentralized frameworks that enable broad inter-organizational collaboration. While Decentralized Autonomous Organizations (DAOs) leverage blockchain for governance and token-based transactions, existing platforms face two major limitations: the absence of decentralized authorization mechanisms and inadequate support for large-scale data management. This paper introduces Agentic DAO, a next-generation DAO architecture that integrates multi-agent authorization and scalable data governance into a blockchain-centric framework. The platform employs a Trust Scoring Agent using Graph Neural Networks (GNNs) to predict transactional reliability and a Data Management Agent to ensure consistency between blockchain and IPFS-based storage. Governance is reinforced through SoulBound Token (SBT)-based credentials to prevent Sybil attacks and enforce fair participation. A prototype implementation demonstrates the feasibility of trust-based access control and robust data synchronization. Experimental results confirm that Agentic DAO enhances transparency, scalability, and fairness compared to conventional DAO models, enabling secure and collaborative engagement in decentralized ecosystems.
Decentralized applications often require reliable information about external events whose outcomes are uncertain at the time of reporting but verifiable after settlement. Existing oracle mechanisms typically rely on trusted data providers, token-weighted voting, staking, dispute games, or prediction-market incentives. These approaches may allocate influence according to wealth, identity count, or discrete votes rather than calibrated informational quality. This paper introduces Proof of Market Consistency (PoMC), an information-weighted oracle mechanism for decentralized event resolution. Admitted reporters submit probabilistic forecasts about event outcomes. Reports are evaluated ex post using strictly proper scoring rules, and each reporter accumulates information capital through a multiplicative update rule based on predictive performance. Oracle outputs are obtained by aggregating reports with information-capital weights. We establish four main properties. First, strictly proper scoring rules provide one-shot incentives for truthful probabilistic reporting. Second, under stationary reporting performance, information capital concentrates on the most accurate reporters while uninformative reporters lose influence. Third, the update rule admits an online-learning interpretation and yields a sublinear regret bound relative to the best reporter in hindsight. Fourth, because purely reputation-based systems are vulnerable to Sybil attacks, we introduce a minimal stake-gated admission rule that bounds identity creation while leaving oracle influence determined by predictive accuracy. PoMC is not a replacement for block-level consensus protocols. Rather, it is a properscoring and online-learning layer for decentralized oracle resolution. The mechanism separates economic admission from informational authority: stake limits identity creation, while calibrated predictive performance determines influence.
The evolution of the World Wide Web is conventionally described in three eras-Web1 (read), Web2 (read-write), and Web3 (read-write-own)-each of which relocated a distinct form of power along the value chain: from publishers, to platforms, to ledger-anchored token holders. This paper introduces and defends a fourth: Web4 (read-write-own-act), in which sovereign ownership is not confined to tokens or ledger records but is co-specified across every operational plane of the network-identity, physical connectivity, compute, naming, developer tooling, application delivery, and consumer hardware-and in which autonomous agents act on behalf of that ownership under cryptographic, time-boxed capability grants. The central original contribution is the Ownership Thesis: the axiom that every layer of the stack must be reducible to a claim controlled by a decentralized identifier the subject holds directly, not one that is issued or held by an operator on the subject's behalf. From this axiom we derive a seven-layer reference architecture and formalize the Synthetic Web as the class of network activity structurally defined by agent-executed action under such capability grants. We position each layer against the relevant standards (W3C DID Core v1.1, W3C Verifiable Credentials Data Model v2.0, Model Context Protocol, Agent-to-Agent v1.0.0, NIST CAISI AI Agent Standards Initiative) and provide a security analysis covering key management, alias unlinkability via BBS+ selective disclosure, Sybil resistance, delegated-authority scope creep, and data-sovereignty enforcement, with explicit identification of open problems that require further verification.
The adoption of decentralized technologies in healthcare introduces new opportunities for secure, patient-centered data management but also brings significant privacy and security challenges. This paper presents a threat modeling approach applied to a Web3-based healthcare platform that integrates blockchain for access logging, a FHIR-compliant server for clinical data, and a backend for identity and access management. Using the LINDDUN privacy threat modeling framework and OWASP Threat Dragon, we identified and prioritized privacy risks based on system architecture and data flows. The results show that threat modeling can provide early insights into regulatory compliance, data exposure, and user privacy concerns. This process can be viewed as a foundational step in the development of digital health systems. While the analysis was focused on a specific use case, the methodology is adaptable to a wide range of applications handling sensitive personal data.
The contemporary digital information ecosystem is suffering from a structural market failure analogous to George Akerlof’s "Market for Lemons." In an era of Generative AI, the marginal cost of producing misinformation has approached zero, while the cost of verifying truth remains high. This asymmetry has created a "Trust Deficit" where high-quality information cannot be reliably distinguished from algorithmic noise. Current remediation strategies are bifurcated between two flawed extremes: Centralized Web2 Platforms (which prioritize scalability at the expense of transparency and are prone to censorship) and Decentralized Web3 Networks (which prioritize immutability but suffer from the "Garbage In, Garbage Out" paradox - permanently recording unverified data). The Trust-Scalability Trilemma: This research posits that decentralized reputation systems face a "Trust-Scalability Trilemma," historically unable to simultaneously achieve Veracity (Accuracy), Scalability (Throughput), and Decentralization (Censorship Resistance). Traditional solutions, such as Token Curated Registries (TCRs), have failed because they rely on synchronous, on-chain voting for every data point, resulting in prohibitive latency and gas costs. The Solution: This paper introduces The Klyrox Protocol, a decentralized middleware designed to resolve this trilemma by decoupling Content Execution from Content Verification. The protocol introduces a novel consensus mechanism, "Proof-of-Klyrox," which combines Optimistic Machine Learning (opML) with Game Theoretic Integrity Bonds. Proof-of-Klyrox is not a blockchain consensus mechanism. It is a layered fraud-detection and incentive framework anchored to existing consensus networks. Scope Note: Protocol V1 focuses exclusively on objective, verifiable claims (e.g., market data, timestamped events, quantifiable metrics). Subjective content quality assessment (e.g., editorial judgment, artistic merit) is explicitly out of scope and scheduled for research in future iterations. The system operates on an "Optimistic" presumption of validity: Optimistic Execution: Content is verified instantly via off-chain AI Oracles, reducing verification costs by an estimated 85-95% compared to traditional on-chain governance models. Cryptoeconomic Security: Users must stake financial collateral (Integrity Bonds) to publish. This creates a "Pay-to-Truth" incentive structure where the cost of generating misinformation strictly exceeds the potential profit. Sybil Resistance: The protocol implements a proprietary Time-Decayed Stake-Weighted (TDSW) algorithm. This scoring engine ensures that influence scales logarithmically with capital (preventing plutocratic capture) and decays exponentially over time (preventing the entrenchment of dormant actors). By financializing reputation into a portable, quantifiable asset class defined as "Epistemic Capital," The Klyrox Protocol offers a scalable blueprint for a self-regulating "Market for Truth." It transforms trust from a subjective social sentiment into an objective, verifiable economic product, providing the necessary infrastructure for the next generation of decentralized media, prediction markets, and AI safety layers. Author's Note: This whitepaper outlines the technical architecture and game-theoretic mechanisms underpinning the concept of "Epistemic Capital," as explored in The Algorithmic Monographs series by Ali Sadhik Shaik (The Algorithmic Invisible Hand, The Republic of Code, The Market for Truth, The Heavy Metal Intelligence and The Synthetic C-Cuite).
The contemporary distributed systems are becoming highly dependent on strong security protocols to secure sensitive information, maintain continuity services, and sustain the trust of people in highly networked systems. With the increasingly advanced cyber threats, centralized security architectures are increasingly limited in ensuring confidentiality, integrity, and availability. This paper explores the idea of Distributed Ledger Technology (DLT) as a new security paradigm to improve trust and resiliency of distributed infrastructures. A systematic review of the literature has been done, with the establishment of a conceptual framework to serve as an assessment of how mechanisms of DLT can be applied to overcome the fundamental security issues. The review summarizes the recent empirical and theoretical research results to evaluate the efficiency of blockchain and ledger technologies associated with securing distributed systems. The findings show that classifications of immutability and cryptographic access controls in data integrity and decentralized consensus models enhance the availability and security of data, respectively. Nonetheless, trade-offs were discovered that were never previously anticipated, specifically, scalability and security. When more transaction throughput is achieved, decentralization tends to decrease, and the risk of vulnerabilities also increases. Also, energy usage and latency are significant issues for large-scale deployments. On the whole, this paper has concluded that even though DLT has significant security advantages, its implementation should be well-coordinated with system needs and operational limitations. The results provide useful advice to secure system designers who want to optimize performance, cost, and resilience of the next generation distributed environment (
Decentralized finance (DeFi) protocols that depend on external settlement facts (whether asset prices from oracle networks or off-chain payment confirmations) must make irreversible on-chain state transitions based on information they cannot directly verify. This settlement verification problem is governed by a fundamental tension between safety (rejecting false claims) and liveness (accepting true claims promptly), mediated by the capital, latency, and trust assumptions a protocol is willing to absorb. We formalize settlement verification as a binary hypothesis-testing problem over an adversarial multi-publisher channel and establish three main results. (1) Oracle Verification Trilemma. For any settlement verification mechanism operating over an adversarial oracle channel with adversarial fraction ϕ δ], where δ is the mechanism’s error-absorption capacity. For hyperbolic funding rates, the singularity at the solvency boundary provides robustness amplification: a β-fraction capacity utilization tolerates oracle errors up to (1 − β) times the total capacity.
Sandro Rodriguez Garzon, Awid Vaziry, Enis Mert Kuzu, Dennis Enrique Gehrmann · 7 authors
A fundamental limitation of current LLM-based AI agents is their inability to build differentiated trust among each other at the onset of an agent-to-agent dialogue. However, autonomous and interoperable trust establishment becomes essential once agents start to operate beyond isolated environments and engage in dialogues across individual or organizational boundaries. A promising way to fill this gap in Agentic AI is to equip agents with long-lived digital identities and introduce tamper-proof and flexible identity-bound attestations of agents, provisioned by commonly trusted third parties and designed for cross-domain verifiability. This article presents a conceptual framework and a prototypical multi-agent system, where each agent is endowed with a self-sovereign digital identity. It combines a unique and ledger-anchored W3C Decentralized Identifier (DID) of an agent with a set of third-party issued W3C Verifiable Credentials (VCs). This enables agents at the start of a dialog to prove ownership of their self-controlled DIDs for authentication purposes and to establish various cross-domain trust relationships through the spontaneous exchange of their self-hosted DID-bound VCs. A comprehensive evaluation of the prototypical implementation demonstrates technical feasibility but also reveals limitations once an agent's LLM is in sole charge to control the respective security procedures.